Author information
1Internal Medicine Unit, Department of Internal Medicine and Geriatrics, Campus Bio-Medico University, Rome, Italy.
2Clinical Medicine and Hepatology Unit, Department of Internal Medicine and Geriatrics, Campus Bio-Medico University, Rome, Italy.
3Department of Molecular and Clinical Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
4Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan.
5Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.
6Massachusetts General Hospital, Boston, Massachusetts, USA.
7Department of Genome Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
8Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
9Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan.
10Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Japan.
11Center for Infectious Disease Education and Research (CiDER), Osaka University, Suita, Japan.
12Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan.
13Clinical Nutrition Unit, Department of Medical and Surgical Science, Magna Graecia University, Catanzaro, Italy.
14Unit of Geriatrics, Department of Internal Medicine and Geriatrics, Campus Bio-Medico University, Rome, Italy.
15Department of Pathophysiology and Transplantation, Università degli Studi di Milano, Milano, Italy.
16Translational Medicine, Biological Resource Center, Department of Transfusion Medicine, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milano, Italy.
17Department of Cardiology, Sahlgrenska University Hospital, Gothenburg, Sweden.
Abstract
Background and aims: The European Association for the Study of the Liver introduced a clinical pathway (EASL CP) for screening significant/advanced fibrosis in people at risk of steatotic liver disease (SLD). We assessed the performance of the first-step FIB4 EASL CP in the general population across different SLD risk groups (MASLD, Met-ALD and ALD) and various age classes.
Methods: We analysed a total of 3372 individuals at risk of SLD from the 2017-2018 National Health and Nutrition Examination Survey (NHANES17-18), projected to 152.3 million U.S. adults, 300,329 from the UK Biobank (UKBB) and 57,644 from the Biobank Japan (BBJ). We assessed liver stiffness measurement (LSM) ≥8 kPa and liver-related events occurring within 3 and 10 years (3/10 year-LREs) as outcomes. We defined MASLD, MetALD, and ALD according to recent international recommendations.
Results: FIB4 sensitivity for LSM ≥ 8 kPa was low (27.7%), but it ranged approximately 80%-90% for 3-year LREs. Using FIB4, 22%-57% of subjects across the three cohorts were identified as candidates for vibration-controlled transient elastography (VCTE), which was mostly avoidable (positive predictive value of FIB4 ≥ 1.3 for LSM ≥ 8 kPa ranging 9.5%-13% across different SLD categories). Sensitivity for LSM ≥ 8 kPa and LREs increased with increasing alcohol intake (ALD>MetALD>MASLD) and age classes. For individuals aged ≥65 years, using the recommended age-adjusted FIB4 cut-off (≥2) substantially reduced sensitivity for LSM ≥ 8 kPa and LREs.
Conclusions: The first-step FIB4 EASL CP is poorly accurate and feasible for individuals at risk of SLD in the general population. It is crucial to enhance the screening strategy with a first-step approach able to reduce unnecessary VCTEs and optimise their yield.